Hands-On Transfer Learning with Python

Deployment and monitoring

Once model development, evaluation, and tuning is complete, along with multiple iterations of improving the results, the final stage of model deployment comes into the picture. Model deployment takes care of aspects such as model persistence, exposing models to other applications through different mechanisms such as API endpoints, and so on, along with developing monitoring strategies.

We live in a dynamic world where everything changes every so often, and the same is true about data and other factors related to our use cases. It is imperative that we put in place monitoring strategies such as regular reports, logs, and tests to keep a check on the performance of our solutions and make changes as and when required. ...